On-Demand Client Deployment And Selection In Federated Learning
Traditional machine learning models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated learning plays nowadays a great role in addressing privacy and technology together by maintain...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | masterThesis |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/14146 https://doi.org/10.26756/th.2022.474 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513468733849600 |
|---|---|
| author | Chahoud, Mario |
| author_facet | Chahoud, Mario |
| author_role | author |
| dc.creator.none.fl_str_mv | Chahoud, Mario |
| dc.date.none.fl_str_mv | 2022-10-27T11:02:22Z 2022-10-27T11:02:22Z 2022 2022-08-18 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/14146 https://doi.org/10.26756/th.2022.474 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Lebanese American University |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Machine learning -- Case studies Computer networks -- Security measures Internet of things -- Security measures Mobile communication systems Data privacy Lebanese American University -- Dissertations Dissertations, Academic |
| dc.title.none.fl_str_mv | On-Demand Client Deployment And Selection In Federated Learning |
| dc.type.none.fl_str_mv | Thesis info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/masterThesis |
| description | Traditional machine learning models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated learning plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients on the fly wherever and whenever needed. In fact, some devices are not available to serve as clients in the federated learning due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in federated learning offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed. |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| id | LAURepo_530fa2c74956b1017fe3a3fa7e534d4e |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/14146 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Lebanese American University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | On-Demand Client Deployment And Selection In Federated LearningChahoud, MarioMachine learning -- Case studiesComputer networks -- Security measuresInternet of things -- Security measuresMobile communication systemsData privacyLebanese American University -- DissertationsDissertations, AcademicTraditional machine learning models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated learning plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients on the fly wherever and whenever needed. In fact, some devices are not available to serve as clients in the federated learning due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in federated learning offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed.1 online resource (x, 49 leaves): col. ill.Bibliography: leaves 46-49.Lebanese American University2022-10-27T11:02:22Z2022-10-27T11:02:22Z20222022-08-18Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/14146https://doi.org/10.26756/th.2022.474http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.phpeninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/141462022-10-27T11:02:51Z |
| spellingShingle | On-Demand Client Deployment And Selection In Federated Learning Chahoud, Mario Machine learning -- Case studies Computer networks -- Security measures Internet of things -- Security measures Mobile communication systems Data privacy Lebanese American University -- Dissertations Dissertations, Academic |
| status_str | publishedVersion |
| title | On-Demand Client Deployment And Selection In Federated Learning |
| title_full | On-Demand Client Deployment And Selection In Federated Learning |
| title_fullStr | On-Demand Client Deployment And Selection In Federated Learning |
| title_full_unstemmed | On-Demand Client Deployment And Selection In Federated Learning |
| title_short | On-Demand Client Deployment And Selection In Federated Learning |
| title_sort | On-Demand Client Deployment And Selection In Federated Learning |
| topic | Machine learning -- Case studies Computer networks -- Security measures Internet of things -- Security measures Mobile communication systems Data privacy Lebanese American University -- Dissertations Dissertations, Academic |
| url | http://hdl.handle.net/10725/14146 https://doi.org/10.26756/th.2022.474 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |